Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse deposit is very important in the Korean real estate market since it is directly related to the residential property sales price and it is a key indicator to predict future real estate market trend. Jeonse deposit data shows a skewed and heteroscedastic distribution and the commonly used mean regression model may be inappropriate for the analysis of Jeonse deposit data. In this paper, we apply a Bayesian quantile regression model to analyze Jeonse deposit data, which is non-parametric and does not require any distributional assumptions. Analysis results show that the quantile regression coefficients of most explanatory variables change dramatically for different quantiles. The regression coefficients of some variables have different signs for different quantiles, implying that even the same variable may affect the Jeonse deposit in the opposite direction depending on the amount of deposit.
Jeonse is a unique property rental system in Korea in which a tenant pays a part of the price of a leased property as a fixed amount security deposit and gets back the entire deposit when the tenant moves out at the end of the tenancy. Jeonse has developed to meet the mutual interests of the house owners and tenants. Tenants use this system as a step to buy their own houses with guaranteed stable residence and house owners utilize it to finance their assets expecting an increase in property prices (Kim, 2015). Fast industrialization followed by the concentration of population in urban areas has increased the demands for residential properties; however, the availability of Jeonse house has not grown enough to meet the demand. As a result, the shortage of house supplies in cities has continued. It is expected that the rising trend of housing prices are likely to continue (Korea Appraisal Board, 2017).
Stability in the real estate market is important to the nation and the public. A continuous rise in property prices carries inherent risks such as a real estate bubble that may lead to a long-term recession with contagious impacts on the global economy as evidenced by the US subprime mortgage crisis or real estate bubble burst in Japan. Also, hard landing in the real estate market would lead to the worsening of the construction industry which accounts for a large part of the national economy and may cause large losses in the domestic financial industry. Furthermore, household debt would also increase while curbing the consumption, creating a vicious cycle of economic deterioration. Considering the critical role of the real estate market to the national economy, it is important to develop useful real estate market information systems that include information on Jeonse deposit and monthly apartment rents (Han, 2016).
Research has been conducted on the Korean real estate market. In particular, price changes for apartments located in Seoul have frequently been study subjects since the Seoul apartment prices are the leading indicator of housing prices nationwide. Most research on apartment prices used a hedonic price model to understand the sales price and the Jeonse deposit of apartments in Seoul (Kim and Lee, 2013; Park and Rhim, 2010) and utilized four types of information (transaction, apartment property, infrastructure, and macro-economic status) as factors that affect housing price (Kim
Most current studies on the real estate market focus on the conditional mean of the apartment prices given the factors that affect the prices, using the
In this paper, we apply a Bayesian quantile regression model to analyze apartment Jeonse deposit data in Seoul. Using the quantile regression model, we study the effect of explanatory variables on a given quantile of the response variable. By changing the quantile, we can investigate the whole distribution, not just the mean, of the response variable (Koenker and Basset, 1978). Also, the quantile regression does not require normality nor homoscedasticity assumptions. We employ a Bayesian approach to fit quantile regression model to Jeonse data using Markov chain Monte Carlo (MCMC). The main advantage of the Bayesian quantile regression is that it is convenient to find the posterior distribution via MCMC algorithms and useful prior information may be utilized (Koenker and Machado, 2001).
Unlike most existing studies on the Korean real estate market data that consider only static factors such as apartment properties and infrastructures (Kim, 2014), we include temporal factors such as the year and the quarter of each year that a transaction occurred, the average amount of Jeonse deposit and the volume of transactions of the previous time point. Inclusion of these time-related variables would help in better understanding Jeonse deposit data, which is clearly time series data.
The rest of this paper is organized as follows. Section 2 presents a brief description of Bayesian quantile regression analysis. Section 3 describes the data and variables used in the study. Section 4 presents analysis results. Section 5 gives the conclusion.
The commonly used mean regression model assumes the following relationship between the response variable
It means that
Let
where
Using the quantile regression model, the overall distribution of the response variable rather than just the mean can be investigated by changing
Consistent estimator of
where
Bayesian quantile regression model assumes
where ALD(
Note that maximizing the likelihood (
For Bayesian inference, the prior distribution of
We focus on the apartments located in Gangnam-gu, Seoul, which is known to be the leading nationwide apartment indicator for Jeonse deposits and sale prices (Kim and Lee, 2013). We use data collected from 2014 to 2016. Previous study of Choi (2017) showed that four major categories which affect house prices are transaction, apartment property, infrastructure and macroeconomic status. We consider these four categories in our study that are obtained from sources given in Table 1. In the table, MOLIT represents the Ministry of Land, Infrastructure and Transportation of Korea.
In this study, a total of 21,547 observations is obtained. There are 345 observations with missing values, which account for a very small fraction (about 1.60%) of the total data. After excluding those missing observations, a total of 21,202 observations is used for the analysis. Every explanatory variable is standardized to have a mean 0 and variance 1 before analysis for ease of comparison.
In each of the four categories, there exist many variables. Table 2 gives the variables finally used in the analysis. We give detailed description of the variables in the rest of this subsection.
We conducted a Bayesian quantile regression analysis for
For the priors of
In Figure 4, the solid line represents the Bayesian quantile regression coefficient estimates and the gray area represents an approximate 95% highest posterior interval (HPD) for each variable. Table 3 presents estimates of the regression coefficients for quantiles 0.1, 0.2, …, 0.9. The figure shows the dramatic changes of the regression coefficients for different quantiles. The great variability of the regression coefficients imply that the same variable has different (even in the opposite direction for some variables) effects in Jeonse deposit depending on the quantiles.
We describe the impact of explanatory variables for each category.
The area has no effect for the quantiles below 0.6 and has negative effects for the quantiles higher than 0.6. The negative effect accelerates as p gets higher. The effect of area on Jeonse deposit is encountered fully in the deposit per area for low quantiles. However, the deposit per area decreases as the area increases for a high deposit per area apartment.
The coefficient of the floor is always positive and bigger in the higher quantiles. It can be under stood that preference for a high floor affect Jeonse deposit more in expensive apartments.
The coefficient of the apartment age is negative and becomes more pronounced in expensive apartments. It appears that the deposit tends to decrease for older apartments and become more expensive for newer apartments.
The number of CCTVs per household and the number of parking lots per household show a similar pattern. The coefficients of the two variables increase, changing from negative to positive.
The time need to reach a subway station, which represents the distance from public transportation, has negative coefficients in all quantiles and seems to have almost the same coefficient except for quantiles over 0.8. The coefficients are close to zero for extremely large quantiles and the impact of public transportation on the deposit is weak. It may be because household income tends to be high in high-priced apartments where residents may have several cars and do not depend on the convenience of public transportation.
The transaction year and the transaction quarter have similar effects on the response variable. The coefficients of both variables have negative values in low quantiles that gradually increase and have large positive values in high quantiles. This implies that deposit decreases for cheap apartments but increases for expensive apartments over time. This bipolar trend accelerates as the quantile
The previous month’s deposit per area has positive effects on the deposit of the current month in all quantiles and the positive effect is particularly strong in the middle quantiles. This means that the higher the deposit in the previous month, the higher the deposit in the current month and this phenomenon is pronounced in the middle quantiles
The previous month’s transaction volume (in numbers) has strong positive effects on Jeonse deposit in high quantiles, while it has a moderate negative effect in lower quantiles.
The existence of parks around apartments has a positive effect, and the existence of department stores also has a positive effect except for
The deposit tends to increase with the increase of population density for quantiles below 0.7. This tendency is stronger for low priced apartments. It is presumed that high deposit means a favorable residential environment for tenants and that the population tends to be concentrated in such environment. However, the deposit decreases with the increase of population density in a high priced apartment. This may imply that a quiet residential environment has higher value in a high-priced apartment.
The coefficient of using infant care centers are always negative, and the negative impacts are relatively smaller at the middle quantile.
The effect of KOSPI decreases for
The market interest rate has a negative effect across all quantiles but the level of impact gets strong for quantiles greater than 0.8. The negative relationship between deposit and market interest rate is clear on low and high ends of quantiles.
Research on the real estate market in Korea have been actively performed since the price of real estate is highly related to national policy and the economy. In this study, we have conducted a Bayesian quantile regression analysis on the Jeonse deposit of apartments located in Gangnam-gu which are presumed to lead the domestic apartment market. We have studied the effect of each explanatory variable on Jeonse deposits for various quantiles.
The study reveals that the average Jeonse deposit of a similar apartment of the previous month, the floor level and the presence of parks always has a positive impact on the deposit. On the other hand, market interest rates, apartment age, time to the nearest subway station and use of infant care facilities have a negative impact. Other factors such as the time of transaction, the number of transactions of the previous month, the KOSPI index, the area, the number of CCTVs, the number of parking spaces per household and the population density have effects in the opposite direction, depending on the quantiles.
Model (
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2016R1A2B4008914).
Resource of data
Data | Web site | URL |
---|---|---|
Apartment property | Apartment Price Management System of the MOLIT | http://www.k-apt.go.kr/ |
Transaction | Real Transaction Price Information System of the MOLIT | http://rt.molit.go.kr/ |
Infrastructure | Seoul Open Data Plaza | http://data.seoul.go.kr/ |
Macroeconomic status | Economic Statistic System of Bank of Korea | http://ecos.bo.or.kr/ |
Data description
Category | Variable | Description |
---|---|---|
Response | price_area | Jeonse price per area |
Apartment property | area | Exclusive area |
floor | floor | |
apart_age | Year of construction of apartment | |
CCTV_n | Number of CCTVs per household | |
parking_n | Number of parking spaces per household | |
metro | Time to the nearest subway station | |
Transaction | year | Year of transaction |
quarter | Quarter of transaction | |
last_n | Number of apartment units of similar floor area (in pyeong) sold/bought in the previous month | |
last_price | Average sales price of apartment units of similar floor area (in pyeong) sold/bought in the previous month | |
Infrastructure | department | Whether a department store is located in the vicinity of subject apartment |
park | Whether a park is located in the vicinity of subject apartment | |
pop_den | Average density of population | |
Macroeconomic status | CPI | Consumer price index of the previous month |
market_rate | Market interest rate of the previous month |
Estimates of the regression coefficient
0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.8 | 0.9 | |
---|---|---|---|---|---|---|---|---|---|
year | −17.87 | −4.77 | 3.26 | 7.81 | 10.53 | 14.28 | 14.78 | 13.43 | 15.06 |
quarter | −4.69 | −3.40 | −1.97 | −1.04 | 0.12 | 1.32 | 1.82 | 0.90 | 4.39 |
last_mean | 78.74 | 87.94 | 91.81 | 96.27 | 97.13 | 95.82 | 91.58 | 83.33 | 73.97 |
last_n | −6.98 | −4.11 | −4.79 | −5.61 | −3.45 | 2.53 | 13.03 | 26.25 | 37.31 |
KOSPI | 0.81 | −0.18 | −0.84 | −1.93 | −3.41 | −3.34 | −3.85 | −1.92 | −0.19 |
market_rate | −37.56 | −34.56 | −32.88 | −32.04 | −32.12 | −31.77 | −35.02 | −42.82 | −46.63 |
area | −1.53 | 0.74 | 0.79 | −0.12 | −0.38 | −0.62 | −2.84 | −6.84 | −8.46 |
floor | 12.53 | 14.61 | 14.08 | 13.07 | 14.38 | 13.87 | 15.4 | 16.9 | 21.45 |
apart_age | −105.26 | −110.01 | −113.99 | −112.76 | −115.32 | −122.02 | −125.6 | −131.89 | −145.60 |
CCTV_n | −8.01 | −6.65 | −5.99 | −2.75 | −2.52 | −2.31 | −1.02 | 3.24 | 4.33 |
parking_n | −8.51 | −1.11 | 3.75 | 9.09 | 13.25 | 16.21 | 22.62 | 25.15 | 26.89 |
metro | −32.4 | −29.36 | −28.74 | −28.81 | −29.55 | −30.02 | −30.13 | −29.49 | −25.35 |
department | 18.3 | 20.77 | 20.96 | 20.95 | 18.02 | 14.13 | 10.41 | 7.21 | 1.84 |
park | 18.78 | 20.52 | 20.41 | 18.47 | 19.4 | 20.16 | 20.91 | 21.27 | 20.80 |
pop_den | 5.30 | 4.24 | 4.47 | 5.48 | 4.71 | 3.91 | −0.97 | −4.19 | −5.27 |
infant | −92.83 | −84.44 | −79.18 | −62.01 | −55.82 | −56.94 | −59.68 | −65.88 | −78.16 |